class_docstr: This class returns normalisation and augmentation layers for use with reader objects.
docstr: returns list of augmentation layers
docstr_tok: ["returns","list","of","augmentation","layers"]
code: def prepare_augmentation_layers(self):
      """
      returns list of augmentation layers
      """
      augmentation_layers = []
      if self.action_param.random_flipping_axes != -1:
          augmentation_layers.append(
              RandomFlipLayer(
                  flip_axes=self.action_param.random_flipping_axes
              )
          )
      if self.action_param.scaling_percentage:
          augmentation_layers.append(
              RandomSpatialScalingLayer(
                  min_percentage=self.action_param.scaling_percentage[
                      0
                  ],
                  max_percentage=self.action_param.scaling_percentage[
                      1
                  ],
                  antialiasing=self.action_param.antialiasing,
                  isotropic=self.action_param.isotropic_scaling,
              )
          )
      if (
          self.action_param.rotation_angle
          or self.action_param.rotation_angle_x
          or self.action_param.rotation_angle_y
          or self.action_param.rotation_angle_z
      ):
          rotation_layer = RandomRotationLayer()
          if self.action_param.rotation_angle:
              rotation_layer.init_uniform_angle(
                  self.action_param.rotation_angle
              )
          else:
              rotation_layer.init_non_uniform_angle(
                  self.action_param.rotation_angle_x,
                  self.action_param.rotation_angle_y,
                  self.action_param.rotation_angle_z,
              )
          augmentation_layers.append(rotation_layer)
      return augmentation_layers
  
file_path: NiftyNet/niftynet/contrib/preprocessors/preprocessing.py
imports: from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.rand_flip import RandomFlipLayer
  from niftynet.layer.rand_rotation import RandomRotationLayer
  from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer
  from niftynet.layer.binary_masking import BinaryMaskingLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
label_desc: Use image node_id 3 for calling the Preprocessing obj's underlying member method code with example usage: obj.prepare_augmentation_layers() and returns: augmentation_layers